标题： 通过注意力引导的图学习进行可解释的分层概念推理 显示英文标题

标题： Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning

摘要： 基于概念的模型（CBMs）是一类深度学习模型，它们通过通过高层次概念解释预测来提供可解释性。 这些模型首先预测概念，然后使用它们执行下游任务。 然而，当前的CBMs仅对最终任务预测提供可解释性，而概念预测本身通常通过黑盒神经网络进行。 为了解决这一限制，我们提出了分层概念记忆推理器（H-CMR），这是一种新的CBM，它为概念和任务预测都提供了可解释性。 H-CMR使用一个学习到的有向无环图来建模概念之间的关系，其中边表示定义概念的逻辑规则。 在推理过程中，H-CMR采用神经注意力机制选择这些规则的一子集，然后将它们分层应用以预测所有概念和最终任务。 实验结果表明，H-CMR在保持最先进性能的同时，通过概念和模型干预实现了强大的人机交互。 前者可以在推理时显著提高准确性，而后者在有背景知识时可以提高训练期间的数据效率。 显示英文摘要

摘要： Concept-Based Models (CBMs) are a class of deep learning models that provide interpretability by explaining predictions through high-level concepts. These models first predict concepts and then use them to perform a downstream task. However, current CBMs offer interpretability only for the final task prediction, while the concept predictions themselves are typically made via black-box neural networks. To address this limitation, we propose Hierarchical Concept Memory Reasoner (H-CMR), a new CBM that provides interpretability for both concept and task predictions. H-CMR models relationships between concepts using a learned directed acyclic graph, where edges represent logic rules that define concepts in terms of other concepts. During inference, H-CMR employs a neural attention mechanism to select a subset of these rules, which are then applied hierarchically to predict all concepts and the final task. Experimental results demonstrate that H-CMR matches state-of-the-art performance while enabling strong human interaction through concept and model interventions. The former can significantly improve accuracy at inference time, while the latter can enhance data efficiency during training when background knowledge is available.